{"id":"W3169373037","doi":"10.1109/access.2021.3085819","title":"A Modified Hybrid Particle Swarm Optimization With Bat Algorithm Parameter Inspired Acceleration Coefficients for Solving Eco-Friendly and Economic Dispatch Problems","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Electric Power System Optimization","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Particle swarm optimization; Economic dispatch; Computer science; Mathematical optimization; Convergence (economics); Electric power system; Algorithm; Hybrid algorithm (constraint satisfaction); Metaheuristic; Acceleration; Renewable energy; Hybrid power; Power (physics); Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001280654,0.0001945069,0.0002252344,0.0000716512,0.0001285607,0.0004824628,0.0001446904,0.00006502355,0.00001013515],"category_scores_gemma":[0.0000254091,0.0001987861,0.00003093744,0.0002089773,0.00001798605,0.0009546014,0.00002831977,0.00007355736,0.000002809731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001528091,"about_ca_system_score_gemma":0.00005565777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003347434,"about_ca_topic_score_gemma":0.00006237655,"domain_scores_codex":[0.9988366,0.00002539728,0.000332902,0.0003579373,0.0001211511,0.000325995],"domain_scores_gemma":[0.9993906,0.00008851558,0.00008793011,0.000228027,0.0001193067,0.00008562129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001333803,0.0000313455,0.0007216721,0.00008761865,0.00004681348,0.000002993549,0.0001200014,0.9930883,0.0005006792,0.00001150295,0.0001343671,0.005241371],"study_design_scores_gemma":[0.001042942,0.0000626438,0.0001342961,0.00004677986,0.00003675167,0.00001654371,0.00001387216,0.9389174,0.05941894,0.00002219664,0.00004332007,0.0002443247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3439613,0.0001017669,0.6549026,0.00002446994,0.0002992192,0.0004861842,0.00002124236,0.0001419357,0.00006119474],"genre_scores_gemma":[0.9802291,0.00006333629,0.01909832,0.00002932281,0.00007341541,0.0002942245,0.0001114964,0.00006348099,0.00003728331],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6362678,"threshold_uncertainty_score":0.8106263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0156128917803038,"score_gpt":0.2355599893035603,"score_spread":0.2199470975232565,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}